Semantic Latent Decomposition with Normalizing Flows for Face Editing
SDFlow proposes a new method for editing StyleGAN faces by decomposing the original latent space using continuous conditional normalizing flows and a semantic encoder. This approach separates different facial attributes into distinct variables, eliminating entanglement between them. By optimizing these components under a mutual information framework, SDFlow allows for precise, disentangled manipulation of specific features.